| タイトル | Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis |
| 本文（外部サイト） | http://hdl.handle.net/2060/20160006273 |
| 著者（英） | Warner, James E.; Leser, William P.; Leser, Patrick E.; Yuan, Fuh-Gwo; Newman, John A.; Hochhalter, Jacob D.; Wawrzynek, Paul A. |
| 著者所属（英） | NASA Langley Research Center |
| 発行日 | 2015-09-01 |
| 言語 | eng |
| 内容記述 | Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions. |
| NASA分類 | Structural Mechanics |
| レポートNO | NF1676L-20700 |
| 権利 | Copyright, Distribution as joint owner in the copyright |
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